Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …
practically minded tomographers. Given that states and unitaries appearing in nature are of …
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey
This survey provides a comprehensive exploration of applications of Topological Data
Analysis (TDA) within neural network analysis. Using TDA tools such as persistent homology …
Analysis (TDA) within neural network analysis. Using TDA tools such as persistent homology …
Super-resolution of X-ray CT images of rock samples by sparse representation: applications to the complex texture of serpentinite
T Omori, S Suzuki, K Michibayashi, A Okamoto - Scientific Reports, 2023 - nature.com
X-ray computed tomography (X-ray CT) has been widely used in the earth sciences, as it is
non-destructive method for providing us the three-dimensional structures of rocks and …
non-destructive method for providing us the three-dimensional structures of rocks and …
Mathematical introduction to deep learning: methods, implementations, and theory
This book aims to provide an introduction to the topic of deep learning algorithms. We review
essential components of deep learning algorithms in full mathematical detail including …
essential components of deep learning algorithms in full mathematical detail including …
[BOOK][B] Metric algebraic geometry
P Breiding, K Kohn, B Sturmfels - 2024 - library.oapen.org
Metric algebraic geometry combines concepts from algebraic geometry and differential
geometry. Building on classical foundations, it offers practical tools for the 21st century …
geometry. Building on classical foundations, it offers practical tools for the 21st century …
[HTML][HTML] The use of machine learning to predict prevalence of subclinical mastitis in dairy sheep farms
Simple Summary We developed a computational model by employing machine learning
methodologies in order to perform predictions regarding the level of prevalence of mastitis in …
methodologies in order to perform predictions regarding the level of prevalence of mastitis in …
Principles of computation by competitive protein dimerization networks
Many biological signaling pathways employ proteins that competitively dimerize in diverse
combinations. These dimerization networks can perform biochemical computations, in which …
combinations. These dimerization networks can perform biochemical computations, in which …
Is K-fold cross validation the best model selection method for Machine Learning?
As a technique that can compactly represent complex patterns, machine learning has
significant potential for predictive inference. K-fold cross-validation (CV) is the most common …
significant potential for predictive inference. K-fold cross-validation (CV) is the most common …
Missing wedge completion via unsupervised learning with coordinate networks
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling
detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its …
detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its …
Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges
Agriculture is essential for global income, poverty reduction, and food security, with crop
yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant …
yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant …